UPDATE – Quantpedia’s Site Maintenance

22.August 2019

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We’ve launched our new website with the updated core back-end technology. Therefore it’s required for all users to change their password (your previous will not work anymore).

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Media Attention and the Low Volatility Effect

18.August 2019

The low volatility factor is a well-known example of a stock trading strategy that contradicts the classical CAPM model. A lot of researchers are trying to come up with an explanation for driving forces behind the volatility effect. One such popular explanation is the ‘attention-grabbing’ hypothesis – which suggests that low-volatility stocks are ‘boring’ and therefore require a premium relative to ‘glittering’ stocks that receive a lot of investor attention. Research paper written by Blitz, Huisman, Swinkels and van Vliet tests this theory and concludes that ‘attention-grabbing’ hypothesis can't be used to explain outperformance of low volatility stocks.

Related to: #7 – Low Volatility Factor Effect in Stocks

Authors: Blitz, Huisman, Swinkels, van Vliet

Title: Media Attention and the Volatility Effect

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3403466

Abstract:

Stocks with low return volatility have high risk-adjusted returns, which might be driven by low media attention for such stocks. Using news coverage data we formally test whether the ‘attention-grabbing’ hypothesis can explain the volatility effect for a sample of international stocks over the period 2001 to 2018. Among stocks with a similar amount of media attention, a low-volatility effect is still present. Among stocks with similar volatility, the amount of media attention is not associated with significantly different risk-adjusted returns. Based on these findings, we reject the hypothesis that media attention is the driving force behind the volatility effect.

Notable quotations from the academic research paper:

"One of the assumptions behind the CAPM is that investors have complete information and that they rationally process this information. However, in reality investors only possess a limited amount of information. Instead of searching for all the information on every possible company, investors may only purchase the stocks of companies that are able to grab their attention. This investor behavior suggests that the prices of attention-grabbing stocks may be temporarily inflated, which subsequently leads to lower returns. Acadedmic research suggests that low-volatility stocks are ‘boring’ and therefore require a premium relative to ‘glittering’ stocks that receive a lot of investor attention.

Since data on aggregate media attention has recently become available, we are able to empirically examine its relation with volatility. In addition to indeed finding a positive association between a stock’s volatility and media attention, we empirically test whether the global low-risk anomaly can be explained by the attention-grabbing hypothesis. The two testable hypotheses are:

1. The low-volatility effect disappears for stocks with high media attention.
2. The low returns for high-volatility stocks are caused primarily by stocks of companies that appear most frequently in the news.

Relation between media attention and volatility

Description

Figure 2 describes the relation between media attention and volatility. After dividing the stocks into five groups based on past volatility, we calculate the average size-adjusted media attention over time. It shows that there is an increasing pattern between volatility and media attention. Media attention increases for stocks in higher volatility groups, and volatility increases for stocks in higher media attention groups. This figure indicates that ‘glittery’ stocks tend to be the stocks with relatively high volatility, while the ‘boring’ stocks that the media does not write about tend to have relatively low volatility.

The positive correlation between media attention and volatility could imply that media attention explains the low-volatility effect, even when there is not a stand-alone media-attention effect. Our objective is to find the driving force behind the volatility effect, and we apply the commonly used double-sorting approach in order to disentangle the two effects. Each month, we independently sort stocks on both characteristics in five groups and then form 25 portfolios on each combination of media attention and volatility. We calculate next month’s return for each group, in which we equally weight each stock within the group.

Double sort - volatility and media attention in stocks

The average excess return of our sample of stocks is 6.89% per annum with a volatility of 15.05%, resulting in a Sharpe ratio of 0.46. The first column further shows that portfolios sorted on return volatility have a higher Sharpe ratio for the low-volatility portfolios and a lower Sharpe ratio for the high-volatility portfolio. The difference is statistically significant with a t-statistic of 3.42. The alpha of the low-volatility minus the high-volatility portfolio is 9.52% per annum and statistically significant with a t-statistic of 3.40.

For media attention to be able to explain the low-volatility effect, we should see that the low-volatility effect disappears for stocks with similar media attention. However, Table 1 shows that for each column with similar media attention, the Sharpe ratios are the highest for low-risk stocks and monotonously decline when volatility increases. The differences in Sharpe ratio are statistically significant, as are the alphas, which range from 12.79% for the low-attention group to 8.05% for the high-attention group.

For groups of similar volatility, the Sharpe ratios and alphas are statistically indistinguishable for stocks regardless of the amount of media attention. Based on these findings, we reject that the attention-grabbing hypothesis explains the volatility effect."


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Metcalfe’s Law in Bitcoin

12.August 2019
Cryptocurrencies are a new asset class, and researchers have just started to understand better fundamental forces which are behind their price action. A new research paper shows that Bitcoin's price can be modeled by Metcalfe's Law. Bitcoin (and other cryptocurrencies) are in this characteristic very similar to Facebook as their value depends on the number of active users – network size

Authors: Peterson

Title: Bitcoin Spreads Like a Virus

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3356098

Abstract:

We illustrate, by way of example, that Bitcoin’s long-term price is non-random and can be modeled as a function of the logistic growth of number of users n over time. Using observed data for both Facebook and Bitcoin, we derive the relationships between price, number of users, and time, and show that the resulting market capitalizations likely follow a Gompertz sigmoid growth function. This function, historically used to describe the growth of biological organisms like bacteria, tumors, and viruses, likely has some application to network economics. We conclude that the long-term growth rate in users has considerable effect on the long-term price of bitcoin.

Notable quotations from the academic research paper:

"This paper offers a simple explanation of price formation in the burgeoning and oft misunderstood cryptocurrency ecosystem. Using bitcoin as an example, we provide convincing empirical evidence that price formation is not a semi-random result of emotional investing but instead is founded on economic principles of value that have only recently begun to be recognized: network economics.

An examination of bitcoin prices offers some interesting observations that directly counter the “value is a mystery” myth. The first is that, as proponents have long argued, the value of a currency is primarily dependent upon use and acceptance of that currency. This hypothesis has been tested and is also apparent from a cursory examination of the relationship between bitcoin’s price and activity associated with the Bitcoin payment network. Specifically, price changes tend to be highly correlated with changes in number of wallets, active addresses, unique addresses, and transaction activity.

Metcalfe’s law is based on the mathematical tautology describing connectivity among n users. Hence, network value V is a function of number of users n. The underlying mathematics for Metcalfe’s law is based on pair-wise connections (e.g., telephony). If there are four people with telephones in a network, there could be a total of 3 + 2 + 1 = 6 connections. As more people join a network, they add to the value of the network nonlinearly; i.e., the value of the network is proportional to the square of the number of users. Metcalfe value V = n * (n-1) / 2.

Facebook and bitcoin price

Facebook is ideally suited to comparison to Bitcoin. The lengths of each data series are nearly the same (about ten years). Both were fairly innovative, though not entirely original (Digicash preceded Bitcoin, MySpace preceded Facebook.) Both faced bans in China, a large potential marketplace. Both received widespread publicity about their adoption. It is rare that one has an opportunity to view the gradual adoption of a currency (or other asset) over time. This is in part because many companies are private during their development stages. But using Equation 1 with well-known Facebook we can see growth follows a pattern similar to that of Bitcoin (Figure 1). Facebook pricing prior to the IPO is unavailable. The value can only be estimated with Metcalfe’s law, and Figure 1 provides the groundwork for that analysis.

daviations from metcalfe's law

Referring back to Figure 3, there are three notable exceptions where bitcoin’s price deviated from the parabolic trend. These are periods of documented price manipulation and eventual resolution to equilibrium. These peaks represent price deviations that can not be accounted for by user-related factors. User related factors are those that a driven by user growth or network usage. Such factors include transactions, active accounts, wallets, nodes, and hash rates. Each of these factors are highly correlated with each other and the effects of changes in these factors are reflected in Metcalfe Value. What remains must be attributable to other factors, which we term non-economic factors. Non-economic factors include wash trading, falsified trade volumes (known as “painting the tape”) and perhaps other activities that may include behavioral impacts.

If n grows at a constant rate, then log(n) is linear. Since we observe for both Facebook and Bitcoin (Figure 7) that log(n) is nonlinear, n grows at a non-constant rate, indicating stages of adoption. This pattern of growth rates, on a cumulative basis, gives rise to a sigmoid function (Gompertz function) – a logistic function that for decades has been used to model viral infection, bacterial growth, tumor growth, and mobile phone proliferation. The most notable application of a Gompertz function to bitcoin pricing to date is Peterson [2018] who used it to model Metcalfe’s affinity coefficient. Using daily data for n proxied as active accounts, we have fit this equation to active addresses in Figure 7.

Grow of bitcoin

"


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Three New Insights from Academic Research Related to Equity Momentum Strategy

4.August 2019

What are the main insights?

– the momentum spread (the difference of the formation-period recent 6-month returns between winners and losers) negatively predicts future momentum profit in the long-term (but not in the following month), the negative predictability is mainly driven by the old momentum spread (old momentum stocks are based on whether a stock has been identified as a momentum stock for more than three months)

– the momentum profits based on total stock returns can be decomposed into three components: a long-term average alpha component that reverses, a stock beta component that accounts for the dynamic market exposure (and momentum crash risk), and a residual return component that drives the momentum effect (and subsumes total-return momentum)

– the profitability and the optimal combination of ranking and holding periods of momentum strategies for a sample of Core and Peripheral European equity markets the profitability vary across markets

1/

Author: Guo

Title: Decomposing Momentum Spread

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3386828

Abstract:

Since momentum arbitrage activity, buying winners and selling losers, effectively enlarges the return spread between these two groups, I connect the momentum spread to future momentum performance. I find that the momentum spread (the difference of the formation-period recent 6-month returns between winners and losers) negatively predicts future momentum profit in the long-term, but not in the following month. I further decompose the momentum spread into the spreads of old or young momentum stocks based on whether a stock has been identified as a momentum stock for more than three months. I show that the negative predictability is mainly driven by the old momentum spread. For the top 20% of the sample period associated with the highest values of old momentum spread, the momentum reversals happen sooner (only six months after formation) and stronger (more than 120 basis points per month from month 7 to month 24 after formation), relative to negligible momentum reversals observed following the bottom 20% period with low old momentum spread. As these old momentum stocks are more likely to be exploited by arbitrageurs, these findings suggest that momentum is amplified by arbitrage activity and excessive arbitrage destabilizes the asset prices and generates strong reversals.

2/

Authors: Hammed, Wu

Title: Decomposing Momentum

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3401656

Abstract:

We decompose the momentum profits based on total stock returns into three components: a long-term average alpha component that reverses, a stock beta component that accounts for the dynamic market exposure (and momentum crash risk), and a residual return component that drives the momentum effect (and subsumes total-return momentum). The variation in total-return momentum across market states and business cycles is attributable to the time-varying performance of the long-term reversal component, while residual-return momentum is invariant over time. Hence, we establish a dichotomy between intermediate-term momentum and long-term reversal: stocks that experience momentum are different from the ones that reverse.

3/

Authors: Slabchenko

Title: Are Momentum Strategies Profitable? Recent Evidence from European Markets

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3397011

Abstract:

This paper examines the profitability of momentum strategies for a sample of Core and Peripheral European equity markets. More specifically, a large number of strategies with different combinations of ranking and holding periods are empirically evaluated for the period between December 1989 to January 2018 for the UK, Germany, French, Sweden, the Netherlands, Italy, Spain, Greece, and Portugal. The results indicate that both the profitability and the optimal combination of ranking and holding periods of momentum strategies vary across markets.
 


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Quantpedia in July 2019

30.July 2019

Dear readers,

With summer at full steam, July was a busy month for the Quantpedia team. Four new Quantpedia Premium strategies have been added into our database and four new related research papers have been included into existing Premium strategies.

Additionally, we have produced 21 new backtests written in QuantConnect code. Our database currently contains 90 strategies with such codes/backtests.

Also, four new blog posts you may find interesting have been published on our Quantpedia blog:

50 Years in PEAD (Post Earnings Announcement Drift) Research
Authors:  Sojka
Title:  50 Years in PEAD Research

Equity Factor Strategies In Frontier Markets
Authors: Zaremba, Maydybura, Czapkiewicz, Arnaut
Title:  Trends Everywhere

Two Versions of CAPM
Author:  Siddiqi
Title:  CAPM: A Tale of Two Versions

Factor Investing in Currency Markets
Authors:  Baku, Fortes, Herve, Lezmi, Malongo, Roncalli, Xu
Title:  Factor Investing in Currency Markets: Does it Make Sense?

Best regards,

Team Quantpedia.com
 


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Factor Investing in Currency Markets

26.July 2019

A new research paper related to multiple currency strategies:

#5 – FX Carry Trade
#8 – Currency Momentum Factor
#9 – Currency Value Factor – PPP Strategy

Authors: Baku, Fortes, Herve, Lezmi, Malongo, Roncalli, Xu

Title: Factor Investing in Currency Markets: Does it Make Sense?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3415700

Abstract:

The concept of factor investing emerged at the end of the 2000s and has completely changed the landscape of equity investing. Today, institutional investors structure their strategic asset allocation around five risk factors: size, value, low beta, momentum and quality. This approach has been extended to multi-asset portfolios and is known as the alternative risk premia model. This framework recognizes that the construction of diversified portfolios cannot only be reduced to the allocation policy between asset classes, such as stocks and bonds. Indeed, diversification is multifaceted and must also consider alternative risk factors. More recently, factor investing has gained popularity in the fixed income universe, even though the use of risk factors is an old topic for modeling the yield curve and pricing interest rate contingent claims. Factor investing is now implemented for managing portfolios of corporate bonds or emerging bonds.

In this paper, we focus on currency markets. The dynamics of foreign exchange rates are generally explained by several theoretical economic models that are commonly presented as competing approaches. In our opinion, they are more complementary and they can be the backbone of a Fama-French-Carhart risk factor model for currencies. In particular, we show that these risk factors
may explain a significant part of time-series and cross-section returns in foreign exchange markets. Therefore, this result helps us to better understand the management of forex portfolios. To illustrate this point, we provide some applications concerning basket hedging, overlay management and the construction of alpha strategies.

Notable quotations from the academic research paper:

"In this paper, we propose analyzing foreign exchange rates using three main risk factors: carry, value and momentum. The choice of these market risk factors is driven by the economic models of foreign exchange rates. For instance, the carry risk factor is based on the uncovered interest rate parity, the value risk factor is derived from equilibrium models of the real exchange rate, and the momentum risk factor bene fits from the importance of technical analysis, trading behavior and overreaction/underreaction patterns. Moreover, analyzing an asset using these three dimensions helps to better characterize the fi nancial patterns that impact an asset: its income, its price and its trend dynamics. Indeed, carry is associated with the yield of the asset, value measures the fair price or the fundamental risk and momentum summarizes the recent price movements.

FX Carry

FX Value

FX Momentum

By using carry, value and momentum risk factors, we are equipped to study the cross-section and time-series of currency returns. In the case of stocks and bonds, academics present their results at the portfolio level because of the large universe of these asset classes. Since the number of currencies is limited, we can show the results at the security level.

For each currency, we can then estimate the sensitivity with respect to each risk factor, the importance of common risk factors, when speci fic risk does matter, etc. We can also connect statistical figures with monetary policies and regimes, illustrating the high interconnectedness of market risk factors and economic risk factors. The primary goal of building an APT model for currencies is to have a framework for analyzing and comparing the behavior of currency returns. This is the main objective of this paper, and a more appropriate title would have been "Factor Analysis of Currency Returns". By choosing the title "Factor Investing in Currency Markets", we emphasize that our risk factor framework can also help to manage currency portfolios as security analysis always comes before investment decisions.

This paper is organized as follows. Section Two is dedicated to the economics of foreign exchange rates. We fi rst introduce the concept of real exchange rate, which is central for understanding the di fferent theories of exchange rate determination. Then, we focus on interest rate and purchasing power parities. Studying monetary models and identifying the statistical properties of currency returns also helps to defi ne the market risk factors, which are presented in Section Three. These risk factors are built using the same approach in terms of portfolio composition and rebalancing. Section Four presents the cross-section and time-series analysis of each currency. We can then estimate a time-varying APT-based model in order to understand the dynamics of currency markets. The results of this dynamic model can be used to manage a currency portfolio. This is why Section Five considers hedging and
overlay management."


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